This application is for continuation of a previously funded (three year) project to develop Smoothing Spline ANOVA (SS-ANOVA) methods for risk factor detection and modeling in large complex data sets. The SS-ANOVA method attempts to model the relationship between an outcome and predictor variables using models that are more flexible than the usual parametric models. For example, the logistic model assumes that the logit is a linear combination or other simple parametric function of the predictors. The SS-ANOVA version assumes only that the logit is a 'smooth' (multivariate) function of the predictors. This smooth function is specified as the minimizer of a penalized log likelihood, defined with the aid of smoothing parameters that are chosen adaptively. A particular data set (the Wisconsin Epidemiologic Study of Diabetic Retinopathy -- WESDR) will be used to develop and test methodology.